Data Processing, Weighting, Non-Responses, MCAR, MAR, and MNAR
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. 1) Missingness mechanisms:
a. Describe the difference between MCAR, MAR and MNAR missingness.
b. Give an example where you would expect to find missingness of each of these types in survey research.
. 2) Describe 3 methods of handing item nonresponse. For each method, list at least one advantage and one disadvantage for this method.
. 3) Describe three reasons why respondents may refuse to participate in a survey. For each reason, provide a suggestion for how to avoid these cases of nonresponse.
. 4) Fill in the table with the appropriate post-stratification weights (w_i4):
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Solution Summary
The solution is very helpful to understand the concept of these types of problem. Step by step solutions are attached for better understanding and clarity to this problem. The terms defined in the solutions are - MCAR, MAR, MNAR, Data processing, weighting, non-responses, and missingness.
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1) Missingness mechanisms:
In any of the survey i.e. either on large scale or on a small scale one will face the problem of the missing data. There are some possibilities that responses for a particular question or field are found to be missing. There could be many reasons for that, such as if questionnaire contains the question regarding smoking and drinking habits, responder may not be comfortable with such questions. Also, some people won't be comfortable to give the response regarding their income. Some of the times, interview cannot be performed because of the absence of the responder, as the responder is not at home at the time of interview.
a) Describe the difference between MCAR, MAR and MNAR missingness.
MCAR stands for Missing Completely at Random. The data where missingness mechanism is not dependent on the variable of the interest or any other observed variable in the dataset, then we say the observations are "Missing Completely at Random" i.e. data is missing at random and it was observed at random.
MAR stands for Missing At Random. The data where missingness mechanism is not dependent on unobserved variables in the data but conditional on some observed variable in the dataset, then we say observations are "Missing At Random" i.e. missing data could be anything but it should be missing.
MNAR stands for Missing Not At Random. Data where missingness mechanism is dependent on the actual values of the missing data, then we say the observations are "Missing Not At Random".
b) Give an ...
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